So....the foundation. The start. The base case. What is the focus of this set of tests? It's just to act as a base case to compare other test results to but at the same time it's also somewhat of a calibration. To understand if the results are fine. To check if the system is working as it should be and if the results make sense.
| Configuration | Values | Notes |
|---|---|---|
| Participant Amount | 3P + 3S | P: Publishers S: Subscribers |
| Publisher Allocation | 1, 1, 1, 0 | VM1, VM2, VM3, VM4 |
| Subscriber Allocation | 1, 1, 1, 0 | VM1, VM2, VM3, VM4 |
| Data Length | 100B | |
| Test Duration | 900S | |
| Test Type | throughput |
Choices were throughput or latency. |
| Latency Count | 1000 | Number of packets between each latency measurement packet. |
| Reliability | reliable |
Choices were reliable or best effort. |
| Communication Method | unicast / multicast |
unicast is one to one communication whilst multicast is one to many. |
| Network Transport | UDPv4 |
As opposed to the default of sharedmem where participants on the same machine don't require samples to travel to routing device. |
So...starting off...what do we want to see? What do we need to show? We have the latency and throughput where the latency is per publisher and the throughput is per subscriber so there is a lot more throughput data than latency. Before we graph anything we should summarise the data.
We have 2 different test configurations and 3 runs of each one. So we have 6 instances of results. The following configurations were run:
Let's take a look at the result files:
average_latencies.csvsub_0_output_average_throughputs.csvsub_1_output_average_throughputs.csvsub_2_output_average_throughputs.csvaverage_latencies.csvsub_0_output_average_throughputs.csvsub_1_output_average_throughputs.csvsub_2_output_average_throughputs.csvFor each test we have the average latency and average throughputs.
Given this, let's start off by plotting the CDFs of the latencies for each run.
from all_functions import *
from set_1_functions import *
ucast = [file for file in get_files('data/v1/set_1') if 'average_latencies' in file and 'unicast' in file]
mcast = [file for file in get_files('data/v1/set_1') if 'average_latencies' in file and 'multicast' in file]
# test_log = [file for file in get_files("data") if 'network_log' in file and '.log' in file][1]
plot_lat_summary_table(ucast)
plot_lat_summary_table(mcast)
files = [file for file in get_files('data/set_1') if 'average' in file and 'forced' in file]
raw_lat_files = [file for file in files if 'average_latencies' in file]
avg_lats = { 'unicast': raw_lat_files[0], 'multicast': raw_lat_files[1] }
fig, ax = plt.subplots(figsize=(20, 10))
df = pd.read_csv(avg_lats['unicast'])
combined_df = pd.concat([ df['run_1_latency'], df['run_2_latency'], df['run_3_latency'] ])
plot_cdf('', df['run_1_latency'], ax, greens[0], 'latency')
plot_cdf('', df['run_2_latency'], ax, greens[2], 'latency')
plot_cdf('', df['run_3_latency'], ax, greens[4], 'latency')
plot_cdf('Unicast Average', combined_df, ax, greens[0], 'average')
ax.axvline(combined_df.mean(), 0, 1, ls="--", color=blues[0], label="Unicast Mean")
unicast_mean = combined_df.mean()
df = pd.read_csv(avg_lats['multicast'])
combined_df = pd.concat([ df['run_1_latency'], df['run_2_latency'], df['run_3_latency'] ])
plot_cdf('', df['run_1_latency'], ax, reds[0], 'latency')
plot_cdf('', df['run_2_latency'], ax, reds[2], 'latency')
plot_cdf('', df['run_3_latency'], ax, reds[4], 'latency')
plot_cdf('Multicast Average', combined_df, ax, reds[0], 'average')
ax.axvline(combined_df.mean(), 0, 1, ls="--", color=oranges[0], label="Multicast Mean")
ax.annotate("+" + format_number(get_percent_diff(combined_df.mean(), unicast_mean)) + "%", (unicast_mean, 0.1), (combined_df.mean() + 5, 0.1), arrowprops={"arrowstyle": "<-", "color": "black"}, fontweight='bold')
_ = ax.get_figure().suptitle("Unicast vs Multicast: Latency CDFs Over 3 15-Minute Runs", fontsize=15, fontweight='bold')
_ = ax.legend()
ax.set_xlabel("Latency ($\mu$s)", fontsize=12, fontweight="bold")
ax.set_ylim(ymin=0, ymax=1)
ax.set_xlim(xmin=100, xmax=1500)
ax.set_xticks(list(ax.get_xticks()) + [unicast_mean, combined_df.mean(), ])
ax.grid()
plt.tight_layout()
tp_files = [file for file in get_files('data/set_1') if 'sub' in file and '.csv' in file and 'average' in file and 'forced_transport' in file]
ucast_files = [file for file in tp_files if 'unicast' in file]
mcast_files = [file for file in tp_files if 'multicast' in file]
# Get unicast latency files
ucast_lats = [file for file in get_files('data/set_1') if 'average' in file and 'throughput' in file and 'sub_1' in file and 'unicast' in file][0]
mcast_lats = [file for file in get_files('data/set_1') if 'average' in file and 'throughput' in file and 'sub_1' in file and 'multicast' in file][0]
plot_summary_table(ucast_lats, mcast_lats)
# Get unicast latency files
ucast = [file for file in get_files('data/set_1') if 'average' in file and 'throughput' in file and 'sub_2' in file and 'unicast' in file][0]
mcast = [file for file in get_files('data/set_1') if 'average' in file and 'throughput' in file and 'sub_2' in file and 'multicast' in file][0]
plot_summary_table(ucast, mcast)
tp_files = [file for file in files if 'throughput' in file]
avg_tps = {
'unicast': [file for file in tp_files if 'unicast' in file],
'multicast': [file for file in tp_files if 'multicast' in file]
}
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(25, 10))
axes[0].set_title("Sub 0 Throughput (Unicast vs Multicast)")
axes[1].set_title("Sub 1 Throughput (Unicast vs Multicast)")
axes[2].set_title("Sub 2 Throughput (Unicast vs Multicast)")
plot_unicast_tp_cdfs(avg_tps['unicast'], axes)
plot_multicast_tp_cdfs(avg_tps['multicast'], axes)
for ax in axes:
ax.legend()
ax.grid()
ax.set_xlabel("Throughput (mbps)")
ax.set_xlim(xmin=40, xmax=80)
ax.set_ylim(ymin=0, ymax=1)
plt.tight_layout()
For Set 2 we change the number of participants whilst comparing Unicast performance with Multicast. This is mainly to see at what point does Multicast outperform Unicast.
In theory, we know that Multicast has 2 "hops" with any amount of participants. This is 1 hop from the publisher to the multicast router and another hope from multicast router to all participants. Meanwhile, unicast has 2n hops for n amount of participants where each participant receives the data sequentially. Therefore, generally speaking, multicast should outperform unicast. However, the data speaks differently...
So we defined some tests to see how the performance is affected when increasing the participants with the following configurations:
| Configuration | Values | Notes |
|---|---|---|
| Participant Amount | 10P + 10S, 25P + 25S, 50P + 50S, 100P + 100S | P: Publishers. S: Subscribers. |
| Publisher Allocation | [3, 2, 3, 2], [6, 7, 6, 7], [12, 13, 12, 13], [25, 25, 25, 25] | Test 1, Test 2, Test 3, Test 4 [VM1, VM2, VM3, VM4] |
| Subscriber Allocation | [3, 2, 3, 2], [6, 7, 6, 7], [12, 13, 12, 13], [25, 25, 25, 25] | Test 1, Test 2, Test 3, Test 4 [VM1, VM2, VM3, VM4] |
| Data Length | 100B | |
| Test Duration | 900S | |
| Test Type | throughput |
Choices were throughput or latency. |
| Latency Count | 1000 | Number of packtes between each latency measurement packet. |
| Reliability | reliable |
Choices were reliable or best effort. |
| Communication Method | unicast / multicast |
unicast is one to one communication while multicast is one to many. |
| Network Transport | UDPv4 |
As opposed to the default of sharedmem where participants on the same machine don't require samples to travel to routing device. |
Basically, we varied unicast and multicast for 10P + 10S, 25P + 25S, 50P + 50S, and 100P + 100S.
For each of the tests (10P + 10S, ..., 100P + 100S) we have the following results as files (applies for both unicast and multicast):
- average_latencies.csv
- sub_0_output_average_throughput.csv
- ...
- sub_n_output_average_throughput.csvTherefore, the next step is actually analysing the results. We start off looking at the CDFs of the latencies:
s2_plot_latency_summary_tables()
set2_plot_latency_cdfs()
def set2_plot_latency_cdfs_per_participant():
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(35, 60))
fig.suptitle("Latency CDFs Per Participant (Unicast vs Multicast)", fontsize=20, fontweight='bold')
s2_lats = [file for file in get_files('data/set_2') if 'average_latencies' in file and '_4_' not in file and 'forced_transport' in file]
s2_ucast_lats = [file for file in s2_lats if 'unicast' in file]
s2_ucast_lats.sort()
for file in s2_ucast_lats:
i = s2_ucast_lats.index(file)
df = pd.read_csv(file)
combined_df = pd.concat([df['run_1_latency'], df['run_2_latency'], df['run_3_latency']])
ax = axes[i]
ax.set_ylim(ymin=0)
ax.set_xlabel("Latency ($\mu$s)")
if i == 0:
# ax.set_xlim(xmin=0, xmax=15000)
ax.set_title("10P + 10S", fontsize=15, fontweight='bold')
elif i == 1:
# ax.set_xlim(xmin=0, xmax=100000)
ax.set_title("25P + 25S", fontsize=15, fontweight='bold')
elif i == 2:
# ax.set_xlim(xmin=0, xmax=300000)
ax.set_title("50P + 50S", fontsize=15, fontweight='bold')
# elif i == 3:
# ax.set_xlim(xmin=0, xmax=600000)
# ax.set_title("75P + 75S", fontsize=15, fontweight='bold')
plot_cdf("", df['run_1_latency'], ax, greens[0], 'normal')
plot_cdf("", df['run_2_latency'], ax, greens[0], 'normal')
plot_cdf("", df['run_3_latency'], ax, greens[0], 'normal')
plot_cdf(get_test_names([file])[0] + " Average", combined_df, ax, greens[0], 'average')
s2_mcast_lats = [file for file in s2_lats if 'multicast' in file]
s2_mcast_lats.sort()
s2_mcast_lats.append(s2_mcast_lats.pop(0))
for file in s2_mcast_lats:
i = s2_mcast_lats.index(file)
df = pd.read_csv(file)
combined_df = pd.concat([df['run_1_latency'], df['run_2_latency'], df['run_3_latency']])
ax = axes[i]
ax.set_ylim(ymin=0)
ax.set_xlabel("Latency ($\mu$s)")
# if i == 0:
# ax.set_xlim(xmin=0, xmax=15000)
# ax.set_title("10P + 10S (Multicast)", fontsize=15, fontweight='bold', color=reds[0])
# elif i == 1:
# ax.set_xlim(xmin=0, xmax=120000)
# ax.set_title("25P + 25S (Multicast)", fontsize=15, fontweight='bold', color=reds[0])
# elif i == 2:
# ax.set_xlim(xmin=0, xmax=300000)
# ax.set_title("50P + 50S (Multicast)", fontsize=15, fontweight='bold', color=reds[0])
# elif i == 3:
# ax.set_xlim(xmin=0, xmax=600000)
# ax.set_title("75P + 75S", fontsize=15, fontweight='bold')
plot_cdf("", df['run_1_latency'], ax, reds[0], 'normal')
plot_cdf("", df['run_2_latency'], ax, reds[0], 'normal')
plot_cdf("", df['run_3_latency'], ax, reds[0], 'normal')
plot_cdf(get_test_names([file])[0] + " Average", combined_df, ax, reds[0], 'average')
for ax in axes:
ax.grid()
ax.legend(loc=2)
plt.tight_layout(pad=5)
print(s2_ucast_lats)
print(s2_mcast_lats)
set2_plot_latency_cdfs_per_participant()
[ 'data/set_2\\2_1_participant_measure_unicast_1_forced_transport\\average_latencies.csv', 'data/set_2\\2_2_participant_measure_unicast_2_forced_transport\\average_latencies.csv', 'data/set_2\\2_3_participant_measure_unicast_3_forced_transport\\average_latencies.csv' ]
[ 'data/set_2\\2_6_participant_measure_multicast_2_forced_transport\\average_latencies.csv' , 'data/set_2\\2_7_participant_measure_multicast_3_forced_transport\\average_latencies.csv' , 'data/set_2\\2_5_participant_measure_multicast_1_forced_transport\\average_latencies.csv' ]
s2_plot_latency_avg_per_participant()
set2_plot_tp_cdfs()
When looking at the latency results of Set 2 we noticed a lot of variation between runs of the same test. Now this was strange because everything should have been the same in terms of the test parameters for each run. It would work by first restarting the VM, then running the test (run 1), then restarting the VM, then running the test (run 2), then restarting the VM, then running the test for the final time (run 3).
The variation between runs would increase corresponding with the increase in participant amount. Below is a bar chart demonstrating the variation between the averages of each run:
s2_rerun_plot_latency_variation_per_participant()
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) File c:\Users\acwh025\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\indexes\base.py:3361, in Index.get_loc(self, key, method, tolerance) <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3359'>3360</a> try: -> <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3360'>3361</a> return self._engine.get_loc(casted_key) <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3361'>3362</a> except KeyError as err: File c:\Users\acwh025\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\_libs\index.pyx:76, in pandas._libs.index.IndexEngine.get_loc() File c:\Users\acwh025\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\_libs\index.pyx:108, in pandas._libs.index.IndexEngine.get_loc() File pandas\_libs\hashtable_class_helper.pxi:5198, in pandas._libs.hashtable.PyObjectHashTable.get_item() File pandas\_libs\hashtable_class_helper.pxi:5206, in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'run_3_latency' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) c:\Users\acwh025\OneDrive - City, University of London\PhD\PAT\PATS Data Processing\Complete Analysis.ipynb Cell 34' in <module> ----> <a href='vscode-notebook-cell:/c%3A/Users/acwh025/OneDrive%20-%20City%2C%20University%20of%20London/PhD/PAT/PATS%20Data%20Processing/Complete%20Analysis.ipynb#ch0000033?line=0'>1</a> s2_rerun_plot_latency_variation_per_participant() File c:\Users\acwh025\OneDrive - City, University of London\PhD\PAT\PATS Data Processing\all_functions.py:922, in s2_rerun_plot_latency_variation_per_participant() <a href='file:///c%3A/Users/acwh025/OneDrive%20-%20City%2C%20University%20of%20London/PhD/PAT/PATS%20Data%20Processing/all_functions.py?line=919'>920</a> to_append["run0"].append(mdf["run_1_latency"].mean()) <a href='file:///c%3A/Users/acwh025/OneDrive%20-%20City%2C%20University%20of%20London/PhD/PAT/PATS%20Data%20Processing/all_functions.py?line=920'>921</a> to_append["run1"].append(mdf["run_2_latency"].mean()) --> <a href='file:///c%3A/Users/acwh025/OneDrive%20-%20City%2C%20University%20of%20London/PhD/PAT/PATS%20Data%20Processing/all_functions.py?line=921'>922</a> to_append["run2"].append(mdf["run_3_latency"].mean()) <a href='file:///c%3A/Users/acwh025/OneDrive%20-%20City%2C%20University%20of%20London/PhD/PAT/PATS%20Data%20Processing/all_functions.py?line=922'>923</a> elif '_5_75_participants' in ucast_file: <a href='file:///c%3A/Users/acwh025/OneDrive%20-%20City%2C%20University%20of%20London/PhD/PAT/PATS%20Data%20Processing/all_functions.py?line=923'>924</a> to_append = data["ucast"] File c:\Users\acwh025\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\frame.py:3458, in DataFrame.__getitem__(self, key) <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/frame.py?line=3455'>3456</a> if self.columns.nlevels > 1: <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/frame.py?line=3456'>3457</a> return self._getitem_multilevel(key) -> <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/frame.py?line=3457'>3458</a> indexer = self.columns.get_loc(key) <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/frame.py?line=3458'>3459</a> if is_integer(indexer): <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/frame.py?line=3459'>3460</a> indexer = [indexer] File c:\Users\acwh025\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\indexes\base.py:3363, in Index.get_loc(self, key, method, tolerance) <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3360'>3361</a> return self._engine.get_loc(casted_key) <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3361'>3362</a> except KeyError as err: -> <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3362'>3363</a> raise KeyError(key) from err <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3364'>3365</a> if is_scalar(key) and isna(key) and not self.hasnans: <a href='file:///c%3A/Users/acwh025/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/indexes/base.py?line=3365'>3366</a> raise KeyError(key) KeyError: 'run_3_latency'
As seen in the bar chart above:
Therefore, we want to investigate why exactly there is this variation between runs despite all known variables being constant. We reran the Set 2 test but this time we tracked the CPU and network usage. Below we first plot the latency CDFs per participant amount on the left and then we plot the network usage on the right of it.
lat_tp_vs_net_per_vm_10p10s_unicast()
lat_tp_vs_net_per_vm_10p10s_multicast()
lat_tp_vs_net_per_vm_25p25s_unicast()
lat_tp_vs_net_per_vm_25p25s_multicast()
lat_tp_vs_net_per_vm_50p50s_unicast()
lat_tp_vs_net_per_vm_50p50s_multicast()
lat_tp_vs_net_per_vm_75p75s_unicast()
lat_tp_vs_net_per_vm_75p75s_multicast()
Because the variations between runs in Set 2 was visible we decided to run some tests for longer amount of time to produce a larger sample and we hope that there is less variation in the runs as the expected value is expected to converge with all runs much better due to the the larger sample size.
We start off by mapping 75P + 75S Unicast/Multicast. On the left we have the old CDFs with 15-minute runs and on the right we have the 24-hour runs.
avg_lat_24hr = [file for file in get_files("data/set_2") if '24_hours' in file and 'average_latencies' in file]
plot_lat_summary_table(avg_lat_24hr)
s2_24hr15m_cdf_comparison()
s2_24hr15m_cdf_comparison_single_plot()
This set of tests focuses on the performance change when DDS is under a DDOS attack. We have two amounts of participants that we can compare with Set 2.
In Set 2 we had:
In Set 3 we have:
Therefore, we can compare Set 2 (1.) with Set 3 (1.) and Set 2 (2.) with Set 3 (2.) since in both cases they have the same amount of legitimate participants. For all Set 3 results we combine all of the measurements into a single list and plot these into CDFs since we are not focusing on the difference between specific runs.
For the DDOS attacks we have taken half of the participants and placed them in a separate domain where they exchange samples of the following ranges:
Therefore, we first look at the comparison between Set 2 and Set 3 with 50 legitimate participants before looking at the comparison between Set 2 and Set 3 with 100 legitimate participants.
def plot_mini_cdf(ax, title, ddos_size, normal_files, ddos_files):
ax.set_title(label=title, fontsize=12, fontweight="bold")
for file in ddos_files[ddos_size]:
df = pd.read_csv(file)
if 'run_1_latency' in df and 'run_2_latency' in df and 'run_3_latency' in df:
combined_df = pd.concat([ df["run_1_latency"], df["run_2_latency"], df["run_3_latency"] ])
else:
combined_df = df['run_1_latency']
if 'unicast' in file:
# plot_cdf('', df["run_1_latency"], ax, greens[0], 'normal')
# plot_cdf('', df["run_2_latency"], ax, greens[0], 'normal')
# plot_cdf('', df["run_3_latency"], ax, greens[0], 'normal')
plot_cdf(ddos_size.upper() + " DDOS Unicast DDS Latency", combined_df, ax, greens[0], 'average')
ax.text(50000, (combined_df.mean() / 200000), "Avg.: " + format_number(combined_df.mean()) + "$\mu$s", color=greens[0], backgroundcolor='white', fontweight='bold', fontsize=10)
ax.annotate('', (60000, (combined_df.mean() / 200000)), (60000, 25934/200000), arrowprops={"arrowstyle": "->", "color": greens[0]})
ax.text(40000, 0.2, "+" + format_number(get_percent_diff(combined_df.mean(), 25934)) + "%", fontweight='bold', color=greens[0])
else:
# plot_cdf('', df["run_1_latency"], ax, reds[0], 'normal')
# plot_cdf('', df["run_2_latency"], ax, reds[0], 'normal')
# plot_cdf('', df["run_3_latency"], ax, reds[0], 'normal')
plot_cdf(ddos_size.upper() + " DDOS Multicast DDS Latency", combined_df, ax, reds[0], 'average')
ax.text(90000, (combined_df.mean() / 200000), "Avg.: " + format_number(combined_df.mean()) + "$\mu$s", color=reds[0], backgroundcolor='white', fontweight='bold', fontsize=10)
ax.annotate('', (100000, (combined_df.mean() / 200000)), (100000, 29149/200000), arrowprops={"arrowstyle": "->", "color": reds[0]})
ax.text(80000, 0.2, "+" + format_number(get_percent_diff(combined_df.mean(), 29149)) + "%", fontweight='bold', color=reds[0])
for file in normal_files:
df = pd.read_csv(file)
if 'run_1_latency' in df and 'run_2_latency' in df and 'run_3_latency' in df:
combined_df = pd.concat([ df["run_1_latency"], df["run_2_latency"], df["run_3_latency"] ])
else:
combined_df = df['run_1_latency']
if 'unicast' in file:
plot_cdf('Normal Unicast DDS Latency', combined_df, ax, blues[0], 'average')
ax.text(50000, (combined_df.mean() / 200000), "Avg.: " + format_number(combined_df.mean()) + "$\mu$s", color=blues[0], backgroundcolor='white', fontweight='bold', fontsize=10)
else:
plot_cdf('Normal Multicast DDS Latency', combined_df, ax, oranges[0], 'average')
ax.text(90000, (combined_df.mean() / 200000), "Avg.: " + format_number(combined_df.mean()) + "$\mu$s", color=oranges[0], backgroundcolor='white', fontweight='bold', fontsize=10)
def s3_plot_ddos_latency_cdf_comparison(type):
"""
Get data for normal test (non-attack)
"""
normal_files = [file for file in get_files("data/set_2") if 'average_latencies' in file and 'forced_transport' in file and ('unicast_2_' in file or 'multicast_2_' in file)]
"""
Get data for ddos test
"""
if 'rerun' in type:
all_ddos_files = [file for file in get_files("data/set_3") if 'average_latencies' in file and 'rerun' in file and ('unicast_2_' in file or 'multicast_2_' in file)]
plot_title = "DDOS Latency CDF Comparison of 6-Hour Tests"
else:
all_ddos_files = [file for file in get_files("data/set_3") if 'average_latencies' in file and 'rerun' not in file and ('unicast_2_' in file or 'multicast_2_' in file)]
plot_title = "DDOS Latency CDF Comparison of 15-Minute Tests"
# print(normal_files)
ddos_files = {
# "1mb": [file for file in all_ddos_files if '1024_kilobyte' in file],
# "512kb": [file for file in all_ddos_files if '512_kilobyte' in file],
"128kb": [file for file in all_ddos_files if '128_kilobyte' in file],
"64kb": [file for file in all_ddos_files if '64_kilobyte' in file],
# "16kb": [file for file in all_ddos_files if '16_kilobyte' in file]
}
fig = plt.figure(figsize=(30, 15))
fig.suptitle(plot_title, fontsize=15, fontweight='bold')
grid = plt.GridSpec(3, 3, figure=fig)
combined = plt.subplot(grid[0:2,0:2])
bot_left = plt.subplot(grid[2, 0])
bot_mid = plt.subplot(grid[2, 1])
top_right = plt.subplot(grid[0, 2])
top_mid = plt.subplot(grid[1, 2])
bot_right = plt.subplot(grid[2, 2])
all = [combined, bot_left, bot_right, bot_mid, top_right, top_mid]
"""
Plot combined latency graph
"""
combined.set_title(label="Latency CDFs Combined", fontsize=12, fontweight='bold')
combined.set_xlim(xmin=0, xmax=200000)
for type in ddos_files:
for file in ddos_files[type]:
df = pd.read_csv(file)
if '16_kilobyte' in file:
title = "DDOS Latency"
else:
title = ""
if 'run_1_latency' in df and 'run_2_latency' in df and 'run_3_latency' in df:
combined_df = pd.concat([ df["run_1_latency"], df["run_2_latency"], df["run_3_latency"] ])
else:
combined_df = df['run_1_latency']
if 'unicast' in file:
title = title + " Unicast" if len(title) > 0 else ""
plot_cdf(title, combined_df, combined, greens[0], 'average')
else:
title = title + " Multicast" if len(title) > 0 else ""
plot_cdf(title, combined_df, combined, reds[0], 'average')
for file in normal_files:
df = pd.read_csv(file)
if 'run_1_latency' in df and 'run_2_latency' in df and 'run_3_latency' in df:
combined_df = pd.concat([ df["run_1_latency"], df["run_2_latency"], df["run_3_latency"] ])
else:
combined_df = df['run_1_latency']
if 'unicast' in file:
plot_cdf('Normal Unicast Latency', combined_df, combined, blues[0], 'average')
else:
plot_cdf('Normal Multicast Latency', combined_df, combined, oranges[0], 'average')
# plot_mini_cdf(bot_left, "16KB DDOS Latency CDFs Per Run", '16kb', normal_files, ddos_files)
plot_mini_cdf(bot_mid, "64KB DDOS Latency CDFs Per Run", '64kb', normal_files, ddos_files)
plot_mini_cdf(bot_right, "128KB DDOS Latency CDFs Per Run", '128kb', normal_files, ddos_files)
# plot_mini_cdf(top_mid, "512KB DDOS Latency CDFs Per Run", '512kb', normal_files, ddos_files)
# plot_mini_cdf(top_right, "1MB DDOS Latency CDFs Per Run", '1mb', normal_files, ddos_files)
for ax in all:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylim(ymin=0, ymax=1)
ax.grid()
# ax.legend()
ax.set_xlabel("Latency ($\mu$s)")
ax.set_xlim(xmin=0, xmax=175000)
plt.tight_layout()
s3_plot_ddos_latency_cdf_comparison('normal')
s3_plot_ddos_latency_cdf_comparison('rerun')
# print([file for file in get_files('data/set_2')])
